DataRobot-Research/drdocs huggingface dataset:
---
title: Personal data detection
description: DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information modeling and predictions.
section_name: Administrator
maturity: public-preview
platform: self-managed-only

---

# Personal data detection {: #personal-data-detection }

!!! info "Availability information: Self-Managed only"
     This public preview feature is only available for US-based Self-Managed AI Platform deployments. Contact your DataRobot representative  or administrator for information on enabling the feature.

In some regulated and specific use cases, the use of personal data as a feature in a model is forbidden. DataRobot automates the detection of specific types of personal data to provide a layer of protection against the inadvertent inclusion of this information in a dataset and prevent its usage at modeling and prediction time.

After a dataset is ingested through the [**AI Catalog**](catalog), you have the option to check each feature for the presence of personal data. The result is a process that checks every cell in a dataset against patterns that DataRobot has developed for identifying this type of information. If found, a warning message is displayed in the **AI Catalog**'s **Info** and **Profile** pages, informing you of the type of personal data detected for each feature and providing sample values to help you make an informed decision on how to move forward. Additionally, DataRobot creates a new feature list&mdash;the equivalent of <em>Informative Features</em> but with all features containing any personal data removed. The new list is named <em>Informative Features - Personal Data Removed</em>.

!!! warning
    There is no guarantee that this tool has identified all instances of personal data. It is intended to supplement your own personal data detection controls.

DataRobot currently supports detection of the following fields:

* Email address
* IPv4 address
* US telephone number
* Social security number

To run personal data detection on a dataset in the **AI Catalog**, go to the **Info** page click **Run Personal Data Detection** on the banner that indicates successful dataset publishing:.

![](images/pii-1.png)

If DataRobot detects personal data in the dataset, a warning message displays. Click **Details** to view more information about the personal data detected; click **Dismiss** to remove the warning and prevent it from being shown again.

![](images/pii-2.png)

Warnings are also highlighted by column on the **Profile** tab:

![](images/pii-3.png)
---
title: Code-first experience
description: Get started with DataRobot's code-first experience. Build and execute notebooks and leverage AI accelerators.
---

# Code-first experience {: #code-first-experience }

Get started with DataRobot's code-first experience. Build and execute notebooks and leverage AI accelerators.

Topic          | Describes...
---------------|---------------
[Get started with code](gs-code) | Understand the ways in which you can get started coding with DataRobot, including authentication steps and notebook creation.
[Get started with AI accelerators](gs-ai)| Learn how you can leverage AI accelerators to quickly engaging in code-first machine learning workflows.
# Version 7.2.8 {: #version-728 }

_January 26, 2022_

The DataRobot v7.2.8 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v7.2.0 release notes for:

* [Features introduced in v7.2.0](v7.2.0-aml)

## Issues fixed in v7.2.8 {: #issues-fixed-in-v728 }

The following issues have been fixed since Enterprise release v7.2.7:

### Enterprise {: #enterprise }

* EP-1366: `pngexport` no longer waits for storage to update.

### Platform {: #platform }

* PLT-5365: Fixes an issue where EMC ECS S3-compatible storage integration resulted in "SSL validation failed."

### Feature Discovery {: #feature-discovery }

* SAFER-4504: Fixes an issue where relationships could not be saved for some JDBC datasets in Feature Discovery projects.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.
---
title: Generate AI Report
description: Generate an AI Report, a high-level overview of your modeling results and insights, to communicate the most important findings of your modeling project to stakeholders.
---

# Generate AI Report {: #generate-ai-report }

Once you complete an Autopilot run, you can generate an AI Report, which communicates the most important findings of your modeling project to stakeholders. The AI Report provides a high-level overview of your modeling results and insights, with particular focus on Trusted AI insights that fall under the dimensions of quality, accuracy, and interpretability.

![](images/ai-report.png)

The report provides accuracy insights for the top performing model, including its speed and cross-validation scores. It also captures interpretability insights in the model's **Feature Impact** histogram, which helps to show which features are driving model decisions.

The AI Report provides these summary details:

* The target and the number of models trained.
* The type of problem, for example, binary or regression.
* The modeling mode, for example, Quick Autopilot.

It also provides details about your data and its quality, including:

* The number of rows of data.
* The number and type of features (for example, categorical, numeric, text, etc.).
* The number of features determined to be informative&mdash;DataRobot removes the features that are not informative.

Availability is as follows:

* Reports can be created for projects built with all modeling modes except manual mode.
* The option is unavailable (the functionality is disabled) for time-aware, unsupervised, and multiclass/multilabel projects.



## Generating an AI Report {: #generating-an-ai-report }

To generate an AI Report for your Autopilot run, on the Leaderboard, click **Menu** and select **AI Report > Generate new report**.

![](images/gen-ai-report.png)

DataRobot automatically downloads the AI Report as a Word document after it is generated.


## Viewing an AI Report {: #viewing-an-ai-report }

To view a previously generated AI Report, on the Leaderboard, click **Menu** and select **AI Report > View report**.

![](images/view-ai-report.png)


## Downloading an AI Report {: #downloading-an-ai-report }

To download a previously generated AI Report, on the Leaderboard, click **Menu** and select **AI Report > Download report**.

![](images/download-ai-report.png)

DataRobot downloads the AI Report as a Word document.
---
title: MLOps
description: DataRobot machine learning operations (MLOps) provides a central hub for you to deploy, monitor, manage, and govern your models in production.

---

# MLOps {: #mlops }

DataRobot MLOps provides a central hub to deploy, monitor, manage, and govern all your models in production. You can deploy models to the production environment of your choice and continuously monitor the health and accuracy of your models, among other metrics.

The following sections describe:

Topic | Describes...
----- | ------
[Deployment](deployment/index) | How to bring models to production by following the workflows provided for all kinds of starting artifacts.
[Deployment settings](deployment-settings/index) | How to use the settings tabs for individual MLOps features to add or update deployment functionality.
[Lifecycle management](manage-mlops/index) | Maintaining model health to minimize inaccurate data, poor performance, or unexpected results from models in production.
[Performance monitoring](monitor/index) | Tracking the performance of models to identify potential issues, such as service errors or model accuracy decay, as soon as possible.
[Governance](governance/index) | Enacting workflow requirements to ensure quality and comply with regulatory obligations.
[MLOps FAQ](mlops-faq) | A list of frequently asked MLOps questions with brief answers linking to the relevant documentation.
---
title: Monitoring external multiclass deployments
description: How to configure the monitoring agent to monitor multiclass models deployed to external prediction environments.
---

#  Monitor external multiclass deployments {: #monitor-external-multiclass-deployments }

Users with multiclass models deployed to external prediction environments can configure the monitoring agent to monitor their deployments. To do so, you must create a deployment in DataRobot with an [external model package](reg-create#register-external-model-packages) for the multiclass model.

When creating an external model package, you can indicate the prediction type for the model as multiclass:

![](images/multi-dep-8.png)

Once indicated, via direct text input or via upload of a CSV or TXT file, provide the target classes for your model (one class per line) in the **Target classes** field:

![](images/multi-dep-9.png)

Once all fields for the model package are defined, click **Create package**. The package populates in the **Model Registry** and is available for use.

When you have configured an external model package for your multiclass model, follow the workflow to [create a deployment](deploy-external-model#deploy-an-external-model-package) with the external model package and [configure it with the monitoring agent](monitoring-agent/index).
Compare the characteristics and capabilities of the two types of custom models below:

Model type        | Characteristics | Capabilities
------------------|-----------------|--------------
Structured        | <ul><li>Uses a target type known to DataRobot (e.g., regression, binary classification, multiclass, and anomaly detection).</li><li>Required to conform to a request/response schema.</li><li>Accepts [structured input and output data](structured-custom-models#structured-custom-model-requirements).</li></ul> | <ul><li>Full deployment capabilities.</li><li>Accepts training data after deployment.</li></ul>
Unstructured      | <ul><li>Uses a custom target type, unknown to DataRobot.</li><li>Not required to conform to a request/response schema.</li><li>Accepts unstructured input and output data.</li></ul> | <ul><li>Limited deployment capabilities. Doesn't support data drift and accuracy statistics, challenger models, or humility rules.</li><li>Doesn't accept training data after deployment.</li></ul>
---
title: Portable Prediction Server
description: Learn how to configure and execute DataRobot's Portable Prediction Server.

---

# Portable Prediction Server {: #portable-prediction-server }

The Portable Prediction Server (PPS) is a remote DataRobot execution environment for DataRobot model packages (`MLPKG` files) distributed as a self-contained Docker image. It can host one or more production models. The models are accessible through DataRobot's Prediction API for predictions and Prediction Explanations.

!!! info "Availability information"
    The Portable Prediction Server is a feature exclusive to DataRobot MLOps. Contact your DataRobot representative for information on enabling it.

| Topic | Describes |
|-------|-----------|
| [Portable Prediction Server](portable-pps)                | Downloading and configuring the Portable Prediction Server. |
| [Portable Prediction Server running modes](pps-run-modes) | Configuring the Portable Prediction Server for single-model or multi-model running mode. |
| [Portable batch predictions](portable-batch-predictions)  | Scoring datasets in batches on a remote environment with the Portable Prediction Server. |
| [Custom model Portable Prediction Server](custom-pps)     | Downloading and configuring the custom model Portable Prediction Server. |

!!! important 
    DataRobot strongly recommends using an Intel CPU to run the Portable Prediction Server. Using non-Intel CPUs can result in prediction inconsistencies, especially in deep learning models like those built with Tensorflow or Keras.
The [metrics values](#metrics-explained) on the ROC curve display might not always match those shown on the Leaderboard. For ROC curve metrics, DataRobot keeps up to 120 of the calculated thresholds that best represent the distribution. Because of this, minute details might be lost. For example, if you select **Maximize MCC** as the [display threshold](threshold#set-the-display-threshold), DataRobot preserves the top 120 thresholds and calculates the maximum among them. This value is usually very close but may not exactly match the metric value.
## About final models {: #about-final-models }

The original ("final") model is trained without holdout data and therefore does not have the most recent data. Instead, it represents the first backtest. This is so that predictions match the insights, coefficients, and other data displayed in the tabs that help evaluate models. (You can verify this by checking the **Final model** representation on the **New Training Period** dialog to view the data your model will use.) If you want to use more recent data, retrain the model using [start and end dates](#start-end).


!!! note
	Be careful retraining on all your data. In Time Series it is very common for historical data to have a negative impact on current predictions. There are a lot of good reasons not to retrain a model for deployment on 100% of the data. Think through how the training window can impact your deployments and ask yourself:

	* "Is all of my data actually relevant to my recent predictions?
	* Are there historical changes or events in my data which may negatively affect how current predictions are made, and that are no longer relevant?"
	* Is anything outside my Backtest 1 training window size _actually_ relevant?

## Retrain before deployment {: #retrain-before-deployment }

Once you have selected a model and unlocked holdout, you may want to retrain the model (although with hyperparameters frozen) to ensure predictive accuracy. Because the original model is trained without the holdout data, it therefore did not have the most recent data. You can verify this by checking the **Final model** representation on the **New Training Period** dialog to view the data your model will use.


To retrain the model, do the following:

1. On the Leaderboard, click the plus sign (**+**) to open the **New Training Period** dialog and change the training period.

2. View the final model and determine whether your model is trained on the most up-to-date data.

3. Enable **Frozen** run by clicking the slider.

4. Select **Start/End Date** and enter the dates for the retraining, including the dates of the holdout data. Remember to use the “+1” method (enter the date immediately after the final date you want to be included).

### Model retraining {: #model-retraining }

Retraining a model on the most recent data* results in the model not having [out-of-sample predictions](data-partitioning#what-are-stacked-predictions), which is what many of the Leaderboard insights rely on. That is, the child (recommended and rebuilt) model trained with the most recent data has no additional samples with which to score the retrained model. Because insights are a key component to both understanding DataRobot's recommendation and facilitating model performance analysis, DataRobot links insights from the parent (original) model to the child (frozen) model.

![](images/otp-child-link.png)


\* This situation is also possible when a model is trained into holdout ("slim-run" models also have no [stacked predictions](data-partitioning#what-are-stacked-predictions)).

The insights affected are:

* ROC Curve
* Lift Chart
* Confusion Matrix
* Stability
* Forecast Accuracy
* Series Insights
* Accuracy Over Time
* Feature Effect
---
title: Relaunch deployments
description: Relaunch an MLOps management agent deployment without changes to the deployment's metadata,

---

# Relaunch management agent deployments

To manually relaunch a management agent deployment without changes to the deployment's metadata, you can trigger a relaunch from the deployment's [Actions menu](actions-menu).

To manually relaunch a deployment, take the following steps:

1. On the [**Deployments**](deploy-inventory) page or any tab within a deployment, next to the name of the deployment you want to relaunch, click the [Actions menu](actions-menu) and click **Relaunch**.

    ![](images/mgmt-agent-relaunch.png)

2. In the **Relaunch deployment** dialog box, click **Relaunch**.

    ![](images/mgmt-agent-relaunch-confirm.png)
---
title: Multiseries segmentation visual overview
dataset_name: N/A
Description: Provides a list of frequently asked questions, and brief answers about multiseries modeling with segmentation in DataRobot. Answers link to more complete documentation.
domain: time series
expiration_date: 3-10-2022
owner: anatolii.stehnii@datarobot.com
url: docs.datarobot.com/docs/modeling/time/ts-reference/segmented-qs.html
---

# Multiseries segmentation visual overview {: #multiseries-segmentation-visual-overview }

<span style="font-size: 1rem">Imagine that you sell avocados&mdash;different kinds (SKUs).</span>

![](images/ts-segment-qs-1.png)

<span style="font-size: 1rem">You want to predict avocado sales, so your target is **Sales**.</span>

![](images/ts-segment-qs-2.png)

<span style="font-size: 1rem">You sell these avocados in different stores, in different regions of the country. So your series ID is **store**. </span>

![](images/ts-segment-qs-3.png)

<span style="font-size: 1rem">Of course, stores sales don’t always have anything to do with one another. Maybe avocados sell often in hot places, and less often in cold places.</span>

![](images/ts-segment-qs-4.png)

<span style="font-size: 1rem">What you really need is a way to group series (stores in different regions) and forecast avocado sales based on that grouping. You can group the series ("stores") based on location and set that as the segment ID ("region"). </span>

![](images/ts-segment-qs-5.png)

<span style="font-size: 1rem">Now you can build the right model for every segment, instead of one model for all. For example, you can model avocados that don’t sell very often with a Zero-Inflated XGBoost model.</span>

![](images/ts-segment-qs-6.png)

<span style="font-size: 1rem">You may even benefit from using a different metric per segment. Metrics are automatically selected based on target distribution.</span>

![](images/ts-segment-qs-7.png)

<span style="font-size: 1rem">How? Multiseries modeling with segmentation.</span>

![](images/ts-segment-qs-8.png)
---
title: Data and sharing
description: This section introduces sharing within DataRobot, including how roles and permissions affect sharing.
---

# Sharing {: #sharing }

The information in this section provides information on data and sharing requirements throughout DataRobot.

Topic | Describes...
----- | ------------
[Sharing](sharing) | How to share assets in DataRobot, including datasets, projects, and deployments.
[Roles and permissions](roles-permissions) | Details roles and permissions at the architecture-, entity-, and authentication-level.
# PostgreSQL {: #postgresql }

## Supported authentication {: #supported-authentication }

- Username/password

## Prerequisites {: #prerequisites }

The following is required before connecting to PostgreSQL in DataRobot:

- PostgreSQL account

## Required parameters {: #required-parameters }

The table below lists the minimum required fields to establish a connection with PostgreSQL:

Required field | Description |  Documentation
--------------- | ---------- |  -----------
`address`       | The server's address used to connect to PostgreSQL.  |   [PostgreSQL documentation](https://jdbc.postgresql.org/documentation/head/connect.html){ target=_blank }

{% include 'includes/data-conn-trouble.md' %}
---
title: Qlik predictions
description: Submit Qlik data for scoring via the prediction API and a sample code snippet.
---

#  Qlik predictions {: #qlik-predictions }

To integrate with Qlik, DataRobot provides a code snippet containing the commands and identifiers necessary to submit Qlik data for scoring using the [Prediction API](dr-predapi).

From a deployment's **Predictions** > **Integrations** tab, click the **Qlik** tile.

![](images/integrations-12.png)

To use the **Qlik Integrations Code Snippet**, follow the sample and make the necessary changes to integrate the model, via the API, into your production application. Click the **Prediction Explanations** checkbox to include prediction explanations (1) alongside the prediction results.

![](images/qlik-snippet.png)

Copy the sample code (2) and modify it as necessary. Once customized, your code snippet is ready for use with the Prediction API.
# Oracle {: #oracle }

There are two data connection types for Oracle Database: Service Name and SID. Use the appropriate parameters for the connection path you use to connect to Oracle Database.

## Supported authentication {: #supported-authentication }

- Username/password for both Service Name and SID

## Prerequisites {: #prerequisites }

The following is required before connecting to Oracle in DataRobot:

- Oracle Database account

## Required parameters {: #required-parameters }

### Service Name {: #service-name}

The table below lists the minimum required fields to establish a connection with Oracle (Service Name):

Required field | Description | Documentation
--------------- | ---------- |  -----------
`address`       | The connection URL that supplies connection information for Oracle.   |  [Oracle documentation](https://docs.oracle.com/cd/E11882_01/appdev.112/e12137/getconn.htm#TDPJD129){ target=_blank }
`serviceName`  | Specifies name used to connect to an instance.  | [Oracle documentation](https://docs.oracle.com/cd/B19306_01/server.102/b14237/initparams188.htm#REFRN10194){ target=_blank }

### SID {: #sid}

The table below lists the minimum required fields to establish a connection with Oracle (SID):

Required field | Description |  Documentation
--------------- | ---------- |  -----------
`address`       | The connection URL that supplies connection information for Oracle.<br><br>**Example:**   |  Oracle   |  [Oracle documentation](https://docs.oracle.com/cd/E11882_01/appdev.112/e12137/getconn.htm#TDPJD129){ target=_blank }
`SID`  | A unique identifier for your database.  |  [Oracle documentation](https://docs.oracle.com/cd/E11882_01/appdev.112/e12137/getconn.htm#TDPJD129){ target=_blank }
`port`  | A unique identifier for your database.  | [Oracle documentation](https://docs.oracle.com/cd/E11882_01/appdev.112/e12137/getconn.htm#TDPJD129){ target=_blank }

{% include 'includes/data-conn-trouble.md' %}
# Version 8.0.15 {: #version-8015 }

_May 22, 2023_

The DataRobot v8.0.15 release includes some fixed issues in the DataRobot Self-Managed AI Platform platform. See the v8.0.0 release notes for:

* [Features introduced in v8.0.0](v8.0.0-aml)

In addition to the issues listed below, DataRobot addressed several outstanding security issues. This release/update is part of DataRobot's continual effort to secure the product and platform.

## Issues fixed in v8.0.15 {: #issues-fixed-in-v8015 }

The following issues have been fixed since release v8.0.14:

### Data management {: #data-management }

- DM-5189: Adds support for listing tables when connecting to the Athena JDBC driver.
- DM-9894: Removes the "Method not supported" error message when using the Treasure Data JDBC driver.

### MLOps {: #mlops }

- MMM-12924: Adds "Selected Prediction Environment" as an additional field, when it differs from the available options.

_All product and company names are trademarks&trade; or registered&reg; trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them_.